Overview

Dataset statistics

Number of variables23
Number of observations3664
Missing cells6752
Missing cells (%)8.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.2 MiB
Average record size in memory631.8 B

Variable types

Categorical13
Numeric10

Alerts

society has a high cardinality: 674 distinct values High cardinality
sector has a high cardinality: 115 distinct values High cardinality
areaWithType has a high cardinality: 2349 distinct values High cardinality
price is highly correlated with price_per_sqft and 7 other fieldsHigh correlation
price_per_sqft is highly correlated with priceHigh correlation
area is highly correlated with price and 6 other fieldsHigh correlation
bedRoom is highly correlated with price and 4 other fieldsHigh correlation
bathroom is highly correlated with price and 5 other fieldsHigh correlation
super_built_up_area is highly correlated with price and 6 other fieldsHigh correlation
built_up_area is highly correlated with price and 3 other fieldsHigh correlation
carpet_area is highly correlated with price and 5 other fieldsHigh correlation
servant room is highly correlated with price and 3 other fieldsHigh correlation
price is highly correlated with bedRoom and 2 other fieldsHigh correlation
area is highly correlated with super_built_up_area and 2 other fieldsHigh correlation
bedRoom is highly correlated with price and 2 other fieldsHigh correlation
bathroom is highly correlated with price and 2 other fieldsHigh correlation
super_built_up_area is highly correlated with price and 6 other fieldsHigh correlation
built_up_area is highly correlated with area and 2 other fieldsHigh correlation
carpet_area is highly correlated with area and 2 other fieldsHigh correlation
servant room is highly correlated with super_built_up_areaHigh correlation
price is highly correlated with price_per_sqft and 4 other fieldsHigh correlation
price_per_sqft is highly correlated with priceHigh correlation
area is highly correlated with price and 5 other fieldsHigh correlation
bedRoom is highly correlated with price and 3 other fieldsHigh correlation
bathroom is highly correlated with price and 5 other fieldsHigh correlation
super_built_up_area is highly correlated with price and 6 other fieldsHigh correlation
built_up_area is highly correlated with area and 2 other fieldsHigh correlation
carpet_area is highly correlated with area and 3 other fieldsHigh correlation
servant room is highly correlated with bathroom and 1 other fieldsHigh correlation
property_type is highly correlated with price and 3 other fieldsHigh correlation
price is highly correlated with property_type and 3 other fieldsHigh correlation
area is highly correlated with built_up_area and 1 other fieldsHigh correlation
bedRoom is highly correlated with property_type and 4 other fieldsHigh correlation
bathroom is highly correlated with property_type and 5 other fieldsHigh correlation
balcony is highly correlated with bathroomHigh correlation
floorNum is highly correlated with property_typeHigh correlation
super_built_up_area is highly correlated with price and 3 other fieldsHigh correlation
built_up_area is highly correlated with areaHigh correlation
carpet_area is highly correlated with areaHigh correlation
servant room is highly correlated with bedRoom and 2 other fieldsHigh correlation
facing has 1042 (28.4%) missing values Missing
super_built_up_area has 1788 (48.8%) missing values Missing
built_up_area has 1988 (54.3%) missing values Missing
carpet_area has 1793 (48.9%) missing values Missing
luxury_score has 121 (3.3%) missing values Missing
area is highly skewed (γ1 = 29.7471759) Skewed
built_up_area is highly skewed (γ1 = 40.54726143) Skewed
carpet_area is highly skewed (γ1 = 24.32687466) Skewed
floorNum has 129 (3.5%) zeros Zeros
luxury_score has 429 (11.7%) zeros Zeros

Reproduction

Analysis started2024-03-23 18:55:03.917080
Analysis finished2024-03-23 18:55:15.839375
Duration11.92 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

property_type
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size247.7 KiB
flat
2821 
house
843 

Length

Max length5
Median length4
Mean length4.230076419
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowflat
2nd rowflat
3rd rowflat
4th rowflat
5th rowflat

Common Values

ValueCountFrequency (%)
flat2821
77.0%
house843
 
23.0%

Length

2024-03-23T18:55:15.923007image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2024-03-23T18:55:15.975005image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
flat2821
77.0%
house843
 
23.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

society
Categorical

HIGH CARDINALITY

Distinct674
Distinct (%)18.4%
Missing1
Missing (%)< 0.1%
Memory size292.9 KiB
independent
481 
tulip violet
 
75
ss the leaf
 
73
shapoorji pallonji joyville gurugram
 
42
dlf new town heights
 
42
Other values (669)
2950 

Length

Max length49
Median length16
Mean length16.87769588
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique307 ?
Unique (%)8.4%

Sample

1st rowmaa bhagwati residency
2nd rowapna enclave
3rd rowtulsiani easy in homes
4th rowsmart world orchard
5th rowparkwood westend

Common Values

ValueCountFrequency (%)
independent481
 
13.1%
tulip violet75
 
2.0%
ss the leaf73
 
2.0%
shapoorji pallonji joyville gurugram42
 
1.1%
dlf new town heights42
 
1.1%
signature global park35
 
1.0%
shree vardhman victoria34
 
0.9%
smart world orchard33
 
0.9%
emaar mgf emerald floors premier32
 
0.9%
paras dews31
 
0.8%
Other values (664)2785
76.0%

Length

2024-03-23T18:55:16.070597image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
independent486
 
5.0%
the350
 
3.6%
dlf219
 
2.3%
park211
 
2.2%
city163
 
1.7%
m3m152
 
1.6%
global152
 
1.6%
emaar151
 
1.6%
signature149
 
1.5%
heights134
 
1.4%
Other values (783)7482
77.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

sector
Categorical

HIGH CARDINALITY

Distinct115
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Memory size265.9 KiB
sohna road
 
154
sector 85
 
108
sector 102
 
106
sector 92
 
100
sector 69
 
93
Other values (110)
3103 

Length

Max length26
Median length9
Mean length9.31441048
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowsector 7
2nd rowsector 3
3rd rowsohna road
4th rowsector 61
5th rowsector 92

Common Values

ValueCountFrequency (%)
sohna road154
 
4.2%
sector 85108
 
2.9%
sector 102106
 
2.9%
sector 92100
 
2.7%
sector 6993
 
2.5%
sector 9089
 
2.4%
sector 6587
 
2.4%
sector 8187
 
2.4%
sector 10986
 
2.3%
sector 7975
 
2.0%
Other values (105)2679
73.1%

Length

2024-03-23T18:55:16.181997image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sector3436
46.7%
road178
 
2.4%
sohna166
 
2.3%
85108
 
1.5%
102106
 
1.4%
92100
 
1.4%
6993
 
1.3%
9089
 
1.2%
6587
 
1.2%
8187
 
1.2%
Other values (107)2904
39.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

price
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct473
Distinct (%)12.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.533247817
Minimum0.07
Maximum31.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.2 KiB
2024-03-23T18:55:16.290065image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Quantile statistics

Minimum0.07
5-th percentile0.37
Q10.95
median1.525
Q32.75
95-th percentile8.5
Maximum31.5
Range31.43
Interquartile range (IQR)1.8

Descriptive statistics

Standard deviation2.979057741
Coefficient of variation (CV)1.175983542
Kurtosis14.95329399
Mean2.533247817
Median Absolute Deviation (MAD)0.725
Skewness3.281159259
Sum9281.82
Variance8.874785026
MonotonicityNot monotonic
2024-03-23T18:55:16.409445image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.2580
 
2.2%
1.564
 
1.7%
1.264
 
1.7%
0.963
 
1.7%
1.162
 
1.7%
1.461
 
1.7%
1.357
 
1.6%
0.9552
 
1.4%
252
 
1.4%
1.648
 
1.3%
Other values (463)3061
83.5%
ValueCountFrequency (%)
0.071
 
< 0.1%
0.161
 
< 0.1%
0.171
 
< 0.1%
0.191
 
< 0.1%
0.28
0.2%
0.216
0.2%
0.228
0.2%
0.231
 
< 0.1%
0.246
0.2%
0.2511
0.3%
ValueCountFrequency (%)
31.51
 
< 0.1%
27.51
 
< 0.1%
262
0.1%
251
 
< 0.1%
241
 
< 0.1%
231
 
< 0.1%
221
 
< 0.1%
203
0.1%
19.52
0.1%
193
0.1%

price_per_sqft
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct2651
Distinct (%)72.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13895.28111
Minimum4
Maximum600000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.2 KiB
2024-03-23T18:55:16.526838image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile4716.15
Q16818
median9022
Q313888.25
95-th percentile33333
Maximum600000
Range599996
Interquartile range (IQR)7070.25

Descriptive statistics

Standard deviation23197.75796
Coefficient of variation (CV)1.669470216
Kurtosis187.1181187
Mean13895.28111
Median Absolute Deviation (MAD)2797.5
Skewness11.44256221
Sum50912310
Variance538135974.4
MonotonicityNot monotonic
2024-03-23T18:55:16.648402image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1000027
 
0.7%
800019
 
0.5%
500017
 
0.5%
1250014
 
0.4%
1111113
 
0.4%
666613
 
0.4%
2222213
 
0.4%
833312
 
0.3%
750012
 
0.3%
600011
 
0.3%
Other values (2641)3513
95.9%
ValueCountFrequency (%)
41
< 0.1%
51
< 0.1%
71
< 0.1%
91
< 0.1%
531
< 0.1%
571
< 0.1%
582
0.1%
601
< 0.1%
611
< 0.1%
791
< 0.1%
ValueCountFrequency (%)
6000001
< 0.1%
4000001
< 0.1%
3157891
< 0.1%
3083331
< 0.1%
2909481
< 0.1%
2833331
< 0.1%
2666661
< 0.1%
2611941
< 0.1%
2453981
< 0.1%
2416661
< 0.1%

area
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct1312
Distinct (%)35.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2886.718068
Minimum50
Maximum875000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.2 KiB
2024-03-23T18:55:16.769616image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile519
Q11232.25
median1731.5
Q32300
95-th percentile4244.35
Maximum875000
Range874950
Interquartile range (IQR)1067.75

Descriptive statistics

Standard deviation23154.90955
Coefficient of variation (CV)8.021188423
Kurtosis943.0595069
Mean2886.718068
Median Absolute Deviation (MAD)531.5
Skewness29.7471759
Sum10576935
Variance536149836.1
MonotonicityNot monotonic
2024-03-23T18:55:16.889973image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
165054
 
1.5%
135048
 
1.3%
180047
 
1.3%
324043
 
1.2%
195043
 
1.2%
270039
 
1.1%
90038
 
1.0%
200033
 
0.9%
225025
 
0.7%
240023
 
0.6%
Other values (1302)3271
89.3%
ValueCountFrequency (%)
504
0.1%
551
 
< 0.1%
561
 
< 0.1%
571
 
< 0.1%
602
0.1%
611
 
< 0.1%
672
0.1%
701
 
< 0.1%
721
 
< 0.1%
761
 
< 0.1%
ValueCountFrequency (%)
8750001
< 0.1%
6428571
< 0.1%
6200001
< 0.1%
5666671
< 0.1%
2155171
< 0.1%
989781
< 0.1%
827811
< 0.1%
655172
0.1%
652611
< 0.1%
582281
< 0.1%

areaWithType
Categorical

HIGH CARDINALITY

Distinct2349
Distinct (%)64.1%
Missing0
Missing (%)0.0%
Memory size426.8 KiB
Plot area 360(301.01 sq.m.)
 
36
Plot area 300(250.84 sq.m.)
 
26
Plot area 200(167.23 sq.m.)
 
19
Plot area 502(419.74 sq.m.)
 
18
Super Built up area 1950(181.16 sq.m.)Carpet area: 1161 sq.ft. (107.86 sq.m.)
 
17
Other values (2344)
3548 

Length

Max length124
Median length38
Mean length54.28848253
Min length12

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1843 ?
Unique (%)50.3%

Sample

1st rowCarpet area: 900 (83.61 sq.m.)
2nd rowCarpet area: 650 (60.39 sq.m.)
3rd rowCarpet area: 595 (55.28 sq.m.)
4th rowCarpet area: 1200 (111.48 sq.m.)
5th rowSuper Built up area 1345(124.95 sq.m.)

Common Values

ValueCountFrequency (%)
Plot area 360(301.01 sq.m.)36
 
1.0%
Plot area 300(250.84 sq.m.)26
 
0.7%
Plot area 200(167.23 sq.m.)19
 
0.5%
Plot area 502(419.74 sq.m.)18
 
0.5%
Super Built up area 1950(181.16 sq.m.)Carpet area: 1161 sq.ft. (107.86 sq.m.)17
 
0.5%
Super Built up area 1578(146.6 sq.m.)17
 
0.5%
Plot area 270(225.75 sq.m.)16
 
0.4%
Super Built up area 1350(125.42 sq.m.)15
 
0.4%
Super Built up area 1650(153.29 sq.m.)Carpet area: 1022.58 sq.ft. (95 sq.m.)14
 
0.4%
Super Built up area 2010(186.74 sq.m.)14
 
0.4%
Other values (2339)3472
94.8%

Length

2024-03-23T18:55:17.027399image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
area5556
18.5%
sq.m3642
12.1%
up3018
 
10.0%
built2317
 
7.7%
super1876
 
6.2%
sq.ft1752
 
5.8%
sq.m.)carpet1184
 
3.9%
sq.m.)built699
 
2.3%
carpet683
 
2.3%
plot667
 
2.2%
Other values (2840)8674
28.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

bedRoom
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct19
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.347434498
Minimum1
Maximum21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.2 KiB
2024-03-23T18:55:17.135112image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median3
Q34
95-th percentile6
Maximum21
Range20
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.879143679
Coefficient of variation (CV)0.5613683197
Kurtosis18.52566211
Mean3.347434498
Median Absolute Deviation (MAD)1
Skewness3.502396113
Sum12265
Variance3.531180968
MonotonicityNot monotonic
2024-03-23T18:55:17.244049image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
31499
40.9%
2941
25.7%
4659
18.0%
5200
 
5.5%
1124
 
3.4%
673
 
2.0%
940
 
1.1%
830
 
0.8%
728
 
0.8%
1227
 
0.7%
Other values (9)43
 
1.2%
ValueCountFrequency (%)
1124
 
3.4%
2941
25.7%
31499
40.9%
4659
18.0%
5200
 
5.5%
673
 
2.0%
728
 
0.8%
830
 
0.8%
940
 
1.1%
1020
 
0.5%
ValueCountFrequency (%)
211
 
< 0.1%
201
 
< 0.1%
192
 
0.1%
182
 
0.1%
1611
0.3%
141
 
< 0.1%
134
 
0.1%
1227
0.7%
111
 
< 0.1%
1020
0.5%

bathroom
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct19
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.41239083
Minimum1
Maximum21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.2 KiB
2024-03-23T18:55:17.349440image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median3
Q34
95-th percentile6
Maximum21
Range20
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.928271315
Coefficient of variation (CV)0.5650792688
Kurtosis17.92003729
Mean3.41239083
Median Absolute Deviation (MAD)1
Skewness3.26707989
Sum12503
Variance3.718230264
MonotonicityNot monotonic
2024-03-23T18:55:17.456782image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
31079
29.4%
21046
28.5%
4818
22.3%
5289
 
7.9%
1155
 
4.2%
6117
 
3.2%
940
 
1.1%
738
 
1.0%
824
 
0.7%
1221
 
0.6%
Other values (9)37
 
1.0%
ValueCountFrequency (%)
1155
 
4.2%
21046
28.5%
31079
29.4%
4818
22.3%
5289
 
7.9%
6117
 
3.2%
738
 
1.0%
824
 
0.7%
940
 
1.1%
109
 
0.2%
ValueCountFrequency (%)
211
 
< 0.1%
203
 
0.1%
184
 
0.1%
173
 
0.1%
167
 
0.2%
142
 
0.1%
134
 
0.1%
1221
0.6%
114
 
0.1%
109
0.2%

balcony
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size237.3 KiB
3+
1161 
3
1074 
2
884 
1
364 
0
181 

Length

Max length2
Median length1
Mean length1.316866812
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row3
4th row2
5th row3

Common Values

ValueCountFrequency (%)
3+1161
31.7%
31074
29.3%
2884
24.1%
1364
 
9.9%
0181
 
4.9%

Length

2024-03-23T18:55:17.570954image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2024-03-23T18:55:17.622479image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
32235
61.0%
2884
 
24.1%
1364
 
9.9%
0181
 
4.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

floorNum
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct43
Distinct (%)1.2%
Missing19
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean6.815363512
Minimum0
Maximum51
Zeros129
Zeros (%)3.5%
Negative0
Negative (%)0.0%
Memory size57.2 KiB
2024-03-23T18:55:17.717887image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median5
Q310
95-th percentile18
Maximum51
Range51
Interquartile range (IQR)8

Descriptive statistics

Standard deviation6.02402662
Coefficient of variation (CV)0.8838892613
Kurtosis4.478673001
Mean6.815363512
Median Absolute Deviation (MAD)3
Skewness1.687804208
Sum24842
Variance36.28889672
MonotonicityNot monotonic
2024-03-23T18:55:17.836165image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
3493
13.5%
2490
13.4%
1350
 
9.6%
4312
 
8.5%
8195
 
5.3%
6183
 
5.0%
10179
 
4.9%
7176
 
4.8%
5169
 
4.6%
9161
 
4.4%
Other values (33)937
25.6%
ValueCountFrequency (%)
0129
 
3.5%
1350
9.6%
2490
13.4%
3493
13.5%
4312
8.5%
5169
 
4.6%
6183
 
5.0%
7176
 
4.8%
8195
 
5.3%
9161
 
4.4%
ValueCountFrequency (%)
511
 
< 0.1%
451
 
< 0.1%
441
 
< 0.1%
432
0.1%
401
 
< 0.1%
392
0.1%
381
 
< 0.1%
352
0.1%
342
0.1%
334
0.1%

facing
Categorical

MISSING

Distinct8
Distinct (%)0.3%
Missing1042
Missing (%)28.4%
Memory size224.6 KiB
East
621 
North-East
621 
North
386 
West
247 
South
231 
Other values (3)
516 

Length

Max length10
Median length5
Mean length6.837147216
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWest
2nd rowWest
3rd rowNorth-East
4th rowSouth-East
5th rowNorth-East

Common Values

ValueCountFrequency (%)
East621
16.9%
North-East621
16.9%
North386
 
10.5%
West247
 
6.7%
South231
 
6.3%
North-West192
 
5.2%
South-East171
 
4.7%
South-West153
 
4.2%
(Missing)1042
28.4%

Length

2024-03-23T18:55:17.952309image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2024-03-23T18:55:18.018584image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
east621
23.7%
north-east621
23.7%
north386
14.7%
west247
 
9.4%
south231
 
8.8%
north-west192
 
7.3%
south-east171
 
6.5%
south-west153
 
5.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

agePossession
Categorical

Distinct6
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size279.2 KiB
Relatively New
1640 
New Property
591 
Moderately Old
558 
Undefined
447 
Old Property
302 

Length

Max length18
Median length14
Mean length13.04012009
Min length9

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRelatively New
2nd rowOld Property
3rd rowNew Property
4th rowUndefined
5th rowUnder Construction

Common Values

ValueCountFrequency (%)
Relatively New1640
44.8%
New Property591
 
16.1%
Moderately Old558
 
15.2%
Undefined447
 
12.2%
Old Property302
 
8.2%
Under Construction126
 
3.4%

Length

2024-03-23T18:55:18.112585image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2024-03-23T18:55:18.169857image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
new2231
32.4%
relatively1640
23.8%
property893
13.0%
old860
 
12.5%
moderately558
 
8.1%
undefined447
 
6.5%
under126
 
1.8%
construction126
 
1.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

super_built_up_area
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct593
Distinct (%)31.6%
Missing1788
Missing (%)48.8%
Infinite0
Infinite (%)0.0%
Mean1924.557862
Minimum89
Maximum10000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.2 KiB
2024-03-23T18:55:18.271115image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Quantile statistics

Minimum89
5-th percentile762.75
Q11478.75
median1828
Q32215
95-th percentile3185
Maximum10000
Range9911
Interquartile range (IQR)736.25

Descriptive statistics

Standard deviation764.5355006
Coefficient of variation (CV)0.3972525407
Kurtosis10.32736574
Mean1924.557862
Median Absolute Deviation (MAD)372
Skewness1.833052855
Sum3610470.55
Variance584514.5316
MonotonicityNot monotonic
2024-03-23T18:55:18.394386image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
165037
 
1.0%
195037
 
1.0%
200025
 
0.7%
157825
 
0.7%
164022
 
0.6%
215022
 
0.6%
190019
 
0.5%
240819
 
0.5%
193018
 
0.5%
281217
 
0.5%
Other values (583)1635
44.6%
(Missing)1788
48.8%
ValueCountFrequency (%)
891
< 0.1%
1451
< 0.1%
1611
< 0.1%
2151
< 0.1%
2161
< 0.1%
3251
< 0.1%
3401
< 0.1%
3521
< 0.1%
3801
< 0.1%
4061
< 0.1%
ValueCountFrequency (%)
100001
< 0.1%
69261
< 0.1%
60001
< 0.1%
58002
0.1%
55141
< 0.1%
53502
0.1%
52002
0.1%
48901
< 0.1%
48571
< 0.1%
48482
0.1%

built_up_area
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
SKEWED

Distinct641
Distinct (%)38.2%
Missing1988
Missing (%)54.3%
Infinite0
Infinite (%)0.0%
Mean2385.206461
Minimum2
Maximum737147
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.2 KiB
2024-03-23T18:55:18.511215image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile240.75
Q11113.5
median1650
Q32400
95-th percentile4657.5
Maximum737147
Range737145
Interquartile range (IQR)1286.5

Descriptive statistics

Standard deviation18016.18433
Coefficient of variation (CV)7.553301828
Kurtosis1654.580198
Mean2385.206461
Median Absolute Deviation (MAD)605
Skewness40.54726143
Sum3997606.029
Variance324582897.6
MonotonicityNot monotonic
2024-03-23T18:55:18.639890image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
180041
 
1.1%
324037
 
1.0%
190034
 
0.9%
135033
 
0.9%
270032
 
0.9%
90028
 
0.8%
160026
 
0.7%
130024
 
0.7%
200024
 
0.7%
170023
 
0.6%
Other values (631)1374
37.5%
(Missing)1988
54.3%
ValueCountFrequency (%)
21
 
< 0.1%
141
 
< 0.1%
301
 
< 0.1%
331
 
< 0.1%
503
0.1%
531
 
< 0.1%
551
 
< 0.1%
561
 
< 0.1%
571
 
< 0.1%
605
0.1%
ValueCountFrequency (%)
7371471
 
< 0.1%
135001
 
< 0.1%
112861
 
< 0.1%
95001
 
< 0.1%
90007
0.2%
87751
 
< 0.1%
82861
 
< 0.1%
8067.81
 
< 0.1%
80001
 
< 0.1%
75002
 
0.1%

carpet_area
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
SKEWED

Distinct731
Distinct (%)39.1%
Missing1793
Missing (%)48.9%
Infinite0
Infinite (%)0.0%
Mean2531.753542
Minimum15
Maximum607936
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.2 KiB
2024-03-23T18:55:18.763046image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile350
Q1845
median1300
Q31790.5
95-th percentile2950
Maximum607936
Range607921
Interquartile range (IQR)945.5

Descriptive statistics

Standard deviation22805.78717
Coefficient of variation (CV)9.007901754
Kurtosis604.2183606
Mean2531.753542
Median Absolute Deviation (MAD)470
Skewness24.32687466
Sum4736910.877
Variance520103928.6
MonotonicityNot monotonic
2024-03-23T18:55:18.885043image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
140042
 
1.1%
180035
 
1.0%
160035
 
1.0%
120031
 
0.8%
150029
 
0.8%
165028
 
0.8%
135027
 
0.7%
130023
 
0.6%
145022
 
0.6%
100022
 
0.6%
Other values (721)1577
43.0%
(Missing)1793
48.9%
ValueCountFrequency (%)
151
 
< 0.1%
331
 
< 0.1%
481
 
< 0.1%
501
 
< 0.1%
591
 
< 0.1%
601
 
< 0.1%
661
 
< 0.1%
721
 
< 0.1%
76.443
0.1%
77.311
 
< 0.1%
ValueCountFrequency (%)
6079361
< 0.1%
5692431
< 0.1%
5143961
< 0.1%
645291
< 0.1%
644121
< 0.1%
581411
< 0.1%
549171
< 0.1%
488111
< 0.1%
459661
< 0.1%
344011
< 0.1%

study room
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size236.2 KiB
0
2967 
1
697 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
02967
81.0%
1697
 
19.0%

Length

2024-03-23T18:55:18.999143image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2024-03-23T18:55:19.055904image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
02967
81.0%
1697
 
19.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

servant room
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size236.2 KiB
0
2345 
1
1319 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
02345
64.0%
11319
36.0%

Length

2024-03-23T18:55:19.128843image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2024-03-23T18:55:19.184769image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
02345
64.0%
11319
36.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

store room
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size236.2 KiB
0
3329 
1
335 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
03329
90.9%
1335
 
9.1%

Length

2024-03-23T18:55:19.259852image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2024-03-23T18:55:19.316862image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
03329
90.9%
1335
 
9.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

pooja room
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size236.2 KiB
0
3014 
1
650 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
03014
82.3%
1650
 
17.7%

Length

2024-03-23T18:55:19.392229image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2024-03-23T18:55:19.449368image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
03014
82.3%
1650
 
17.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

others
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size236.2 KiB
0
3262 
1
402 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
03262
89.0%
1402
 
11.0%

Length

2024-03-23T18:55:19.523531image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2024-03-23T18:55:19.581493image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
03262
89.0%
1402
 
11.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

furnishing_type
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size236.2 KiB
1
2406 
0
1051 
2
 
207

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
12406
65.7%
01051
28.7%
2207
 
5.6%

Length

2024-03-23T18:55:19.655832image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2024-03-23T18:55:19.713807image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
12406
65.7%
01051
28.7%
2207
 
5.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

luxury_score
Real number (ℝ≥0)

MISSING
ZEROS

Distinct161
Distinct (%)4.5%
Missing121
Missing (%)3.3%
Infinite0
Infinite (%)0.0%
Mean72.39204064
Minimum0
Maximum174
Zeros429
Zeros (%)11.7%
Negative0
Negative (%)0.0%
Memory size57.2 KiB
2024-03-23T18:55:19.797313image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q133
median60
Q3111
95-th percentile174
Maximum174
Range174
Interquartile range (IQR)78

Descriptive statistics

Standard deviation53.26317381
Coefficient of variation (CV)0.7357600828
Kurtosis-0.9100305762
Mean72.39204064
Median Absolute Deviation (MAD)39
Skewness0.4418276154
Sum256485
Variance2836.965685
MonotonicityNot monotonic
2024-03-23T18:55:19.919330image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0429
 
11.7%
49341
 
9.3%
174194
 
5.3%
16555
 
1.5%
3854
 
1.5%
4452
 
1.4%
7251
 
1.4%
6046
 
1.3%
3744
 
1.2%
1541
 
1.1%
Other values (151)2236
61.0%
(Missing)121
 
3.3%
ValueCountFrequency (%)
0429
11.7%
56
 
0.2%
66
 
0.2%
741
 
1.1%
829
 
0.8%
99
 
0.2%
126
 
0.2%
1310
 
0.3%
1412
 
0.3%
1541
 
1.1%
ValueCountFrequency (%)
174194
5.3%
1691
 
< 0.1%
1689
 
0.2%
16721
 
0.6%
16610
 
0.3%
16555
 
1.5%
1613
 
0.1%
16027
 
0.7%
15923
 
0.6%
15833
 
0.9%

Interactions

2024-03-23T18:55:14.018276image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-23T18:55:06.464655image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-23T18:55:07.298327image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-23T18:55:08.144552image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-23T18:55:08.954345image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-23T18:55:09.817107image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-23T18:55:10.682192image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-23T18:55:11.497686image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-23T18:55:12.316667image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-23T18:55:13.188079image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-23T18:55:14.099799image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-23T18:55:06.551629image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-23T18:55:07.381883image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-23T18:55:08.223238image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-23T18:55:09.039473image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-23T18:55:09.902255image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-23T18:55:10.761984image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-23T18:55:11.577209image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-23T18:55:12.407016image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-23T18:55:13.267381image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-23T18:55:14.185386image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-23T18:55:06.636164image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-23T18:55:07.465306image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-23T18:55:08.303626image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-23T18:55:09.127781image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-23T18:55:09.989324image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-23T18:55:10.844945image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-23T18:55:11.660985image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-23T18:55:12.499836image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-23T18:55:13.353617image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-23T18:55:14.264938image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-23T18:55:06.713748image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-23T18:55:07.543515image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-23T18:55:08.376262image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-23T18:55:09.207479image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-23T18:55:10.073488image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-23T18:55:10.920136image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-23T18:55:11.740383image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-23T18:55:12.587641image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-23T18:55:13.435676image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-23T18:55:14.355594image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-23T18:55:06.802123image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-23T18:55:07.633877image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-23T18:55:08.463078image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-23T18:55:09.297308image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-23T18:55:10.164486image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-23T18:55:11.008539image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-23T18:55:11.829143image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-23T18:55:12.686368image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-23T18:55:13.522408image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-23T18:55:14.447128image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-23T18:55:06.891029image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-23T18:55:07.723839image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-23T18:55:08.549664image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-23T18:55:09.388501image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-23T18:55:10.253775image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-23T18:55:11.095086image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-23T18:55:11.912370image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-23T18:55:12.777939image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-23T18:55:13.608638image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-23T18:55:14.528343image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-23T18:55:06.969868image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-23T18:55:07.806244image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-23T18:55:08.626693image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-23T18:55:09.472604image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-23T18:55:10.337482image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-23T18:55:11.171738image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-23T18:55:11.989004image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-23T18:55:12.861586image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-23T18:55:13.687658image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-23T18:55:14.611619image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-23T18:55:07.048587image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-23T18:55:07.887142image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-23T18:55:08.704376image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-23T18:55:09.555495image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-23T18:55:10.417391image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-23T18:55:11.248043image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-23T18:55:12.068108image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-23T18:55:12.935353image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-23T18:55:13.770924image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-23T18:55:14.699312image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-23T18:55:07.133091image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-23T18:55:07.973397image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-23T18:55:08.788647image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-23T18:55:09.645196image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-23T18:55:10.508026image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-23T18:55:11.334331image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-23T18:55:12.142563image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-23T18:55:13.023281image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-23T18:55:13.847675image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-23T18:55:14.785144image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-23T18:55:07.215265image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-23T18:55:08.057039image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-23T18:55:08.871520image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-23T18:55:09.729579image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-23T18:55:10.592523image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-23T18:55:11.414713image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-23T18:55:12.225598image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-23T18:55:13.098779image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-03-23T18:55:13.931778image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Correlations

2024-03-23T18:55:20.011126image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2024-03-23T18:55:20.151220image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2024-03-23T18:55:20.285981image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2024-03-23T18:55:20.413528image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2024-03-23T18:55:20.526068image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2024-03-23T18:55:15.352617image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-23T18:55:15.721092image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

First rows

property_typesocietysectorpriceprice_per_sqftareaareaWithTypebedRoombathroombalconyfloorNumfacingagePossessionsuper_built_up_areabuilt_up_areacarpet_areastudy roomservant roomstore roompooja roomothersfurnishing_typeluxury_score
0flatmaa bhagwati residencysector 70.455000.0900.0Carpet area: 900 (83.61 sq.m.)2214.0WestRelatively NewNaNNaN900.000000128.0
1flatapna enclavesector 30.507692.0650.0Carpet area: 650 (60.39 sq.m.)2211.0WestOld PropertyNaNNaN650.000000037.0
2flattulsiani easy in homessohna road0.406722.0595.0Carpet area: 595 (55.28 sq.m.)22312.0NaNNew PropertyNaNNaN595.000000136.0
3flatsmart world orchardsector 611.4712250.01200.0Carpet area: 1200 (111.48 sq.m.)2222.0NaNUndefinedNaNNaN1200.010000176.0
4flatparkwood westendsector 920.705204.01345.0Super Built up area 1345(124.95 sq.m.)2235.0NaNUnder Construction1345.0NaNNaN1000010.0
5flatsignature global infinity mallsector 360.416269.0654.0Built Up area: 654 (60.76 sq.m.)2233.0NaNUndefinedNaN654.0NaN0000010.0
6flatthe cocoondwarka expressway2.0013333.01500.0Super Built up area 1500(139.35 sq.m.)3335.0NaNNew Property1500.0NaNNaN0000010.0
7flatats triumphsector 1041.807860.02290.0Carpet area: 2290 (212.75 sq.m.)34314.0NaNNew PropertyNaNNaN2290.000000160.0
8flatvatika xpressionssector 88b1.108148.01350.0Built Up area: 1350 (125.42 sq.m.)Carpet area: 1050 sq.ft. (97.55 sq.m.)243+2.0North-EastUnder ConstructionNaN1350.01050.010000158.0
9flatraheja revantasector 784.7516885.02813.0Built Up area: 2813 (261.34 sq.m.)33231.0NaNUndefinedNaN2813.0NaN010001100.0

Last rows

property_typesocietysectorpriceprice_per_sqftareaareaWithTypebedRoombathroombalconyfloorNumfacingagePossessionsuper_built_up_areabuilt_up_areacarpet_areastudy roomservant roomstore roompooja roomothersfurnishing_typeluxury_score
3654houseindependentsector 313.5024155.01449.0Plot area 161(134.62 sq.m.)4332.0South-WestModerately OldNaN1449.0NaN001000NaN
3655houseindependentsector 465.6523870.02367.0Plot area 263(219.9 sq.m.)863+3.0South-WestModerately OldNaN2367.0NaN010000NaN
3656houseindependentsector 463.5524500.01449.0Plot area 161(134.62 sq.m.)543+3.0North-WestModerately OldNaN1449.0NaN010000NaN
3657houseindependentsector 463.6024845.01449.0Plot area 161(134.62 sq.m.)553+3.0South-EastModerately OldNaN1449.0NaN010000NaN
3658houseindependentsector 553.1020026.01548.0Plot area 172(143.81 sq.m.)543+2.0North-EastModerately OldNaN1548.0NaN011000NaN
3659houseindependentsector 574.7528787.01650.0Plot area 1600(148.64 sq.m.)Built Up area: 1700 sq.ft. (157.94 sq.m.)Carpet area: 1650 sq.ft. (153.29 sq.m.)3332.0North-WestModerately OldNaN1700.01650.0001000NaN
3660housedlf city phase 1sector 265.5030556.01800.0Plot area 200(167.23 sq.m.)4432.0North-EastModerately OldNaN1800.0NaN110101NaN
3661housedlf city plots phase 2sector 254.2531481.01350.0Plot area 150(125.42 sq.m.)3232.0NorthOld PropertyNaN1350.0NaN100001NaN
3662housedlf city phase 1sector 264.5033333.01350.0Plot area 150(125.42 sq.m.)3322.0EastModerately OldNaN1350.0NaN110001NaN
3663housedlf city phase 1sector 263.2533129.0981.0Plot area 109(91.14 sq.m.)3332.0WestOld PropertyNaN981.0NaN100001NaN